mirror of
https://github.com/saymrwulf/pytorch.git
synced 2026-05-14 20:57:59 +00:00
dab272eed8
Move the pytest cache downloading into the build step and store it in additional ci files so that it stays consistent during sharding. Only build env is taken into account now instead of also test config since we might not have the test config during build time, making it less specific, but I also think this might be better since tests are likely to fail across the same test config (I also think it might be worth not even looking at build env but thats a different topic) Each cache upload should only include information from the current run. Do not merge current cache with downloaded cache during upload (shouldn't matter anyways since the downloaded cache won't exist at the time) From what I cant tell of the s3 retention policy, pytest cache files will be deleted after 30 days (cc @ZainRizvi to confirm), so we never have to worry about space or pulling old versions. Pull Request resolved: https://github.com/pytorch/pytorch/pull/113804 Approved by: https://github.com/ZainRizvi
372 lines
16 KiB
YAML
372 lines
16 KiB
YAML
name: linux-test
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on:
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workflow_call:
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inputs:
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build-environment:
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required: true
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type: string
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description: Top-level label for what's being built/tested.
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test-matrix:
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required: true
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type: string
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description: JSON description of what test configs to run.
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docker-image:
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required: true
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type: string
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description: Docker image to run in.
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sync-tag:
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required: false
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type: string
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default: ""
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description: |
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If this is set, our linter will use this to make sure that every other
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job with the same `sync-tag` is identical.
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timeout-minutes:
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required: false
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type: number
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default: 240
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description: |
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Set the maximum (in minutes) how long the workflow should take to finish
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use-gha:
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required: false
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type: string
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default: ""
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description: If set to any value, upload to GHA. Otherwise upload to S3.
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dashboard-tag:
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required: false
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type: string
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default: ""
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secrets:
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HUGGING_FACE_HUB_TOKEN:
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required: false
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description: |
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HF Auth token to avoid rate limits when downloading models or datasets from hub
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env:
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GIT_DEFAULT_BRANCH: ${{ github.event.repository.default_branch }}
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jobs:
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test:
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# Don't run on forked repos or empty test matrix
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if: github.repository_owner == 'pytorch' && toJSON(fromJSON(inputs.test-matrix).include) != '[]'
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strategy:
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matrix: ${{ fromJSON(inputs.test-matrix) }}
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fail-fast: false
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runs-on: ${{ matrix.runner }}
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timeout-minutes: ${{ matrix.mem_leak_check == 'mem_leak_check' && 600 || inputs.timeout-minutes }}
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steps:
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- name: Setup SSH (Click me for login details)
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uses: pytorch/test-infra/.github/actions/setup-ssh@main
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if: ${{ !contains(matrix.runner, 'gcp.a100') }}
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with:
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github-secret: ${{ secrets.GITHUB_TOKEN }}
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instructions: |
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All testing is done inside the container, to start an interactive session run:
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docker exec -it $(docker container ps --format '{{.ID}}') bash
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- name: Checkout PyTorch
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uses: pytorch/pytorch/.github/actions/checkout-pytorch@main
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- name: Setup Linux
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uses: ./.github/actions/setup-linux
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- name: Calculate docker image
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id: calculate-docker-image
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uses: pytorch/test-infra/.github/actions/calculate-docker-image@main
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with:
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docker-image-name: ${{ inputs.docker-image }}
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- name: Pull docker image
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uses: pytorch/test-infra/.github/actions/pull-docker-image@main
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with:
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docker-image: ${{ steps.calculate-docker-image.outputs.docker-image }}
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- name: Install nvidia driver, nvidia-docker runtime, set GPU_FLAG
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id: install-nvidia-driver
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uses: pytorch/test-infra/.github/actions/setup-nvidia@main
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if: contains(inputs.build-environment, 'cuda') && !contains(matrix.config, 'nogpu')
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- name: Lock NVIDIA A100 40GB Frequency
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run: |
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sudo nvidia-smi -pm 1
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sudo nvidia-smi -ac 1215,1410
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nvidia-smi
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if: contains(matrix.runner, 'a100')
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- name: Start monitoring script
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id: monitor-script
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shell: bash
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continue-on-error: true
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run: |
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python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84
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python3 -m tools.stats.monitor > usage_log.txt 2>&1 &
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echo "monitor-script-pid=${!}" >> "${GITHUB_OUTPUT}"
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- name: Download build artifacts
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uses: ./.github/actions/download-build-artifacts
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with:
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name: ${{ inputs.build-environment }}
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- name: Parse ref
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id: parse-ref
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run: .github/scripts/parse_ref.py
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- name: Get workflow job id
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id: get-job-id
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uses: ./.github/actions/get-workflow-job-id
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if: always()
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with:
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github-token: ${{ secrets.GITHUB_TOKEN }}
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- name: Check for keep-going label and re-enabled test issues
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# This uses the filter-test-configs action because it conviniently
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# checks for labels and re-enabled test issues. It does not actually do
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# any filtering. All filtering is done in the build step.
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id: keep-going
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uses: ./.github/actions/filter-test-configs
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with:
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github-token: ${{ secrets.GITHUB_TOKEN }}
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test-matrix: ${{ inputs.test-matrix }}
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job-name: ${{ steps.get-job-id.outputs.job-name }}
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- name: Set Test step time
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id: test-timeout
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shell: bash
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env:
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JOB_TIMEOUT: ${{ matrix.mem_leak_check == 'mem_leak_check' && 600 || inputs.timeout-minutes }}
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run: |
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echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}"
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- name: Test
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id: test
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timeout-minutes: ${{ fromJson(steps.test-timeout.outputs.timeout) }}
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env:
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BUILD_ENVIRONMENT: ${{ inputs.build-environment }}
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PR_NUMBER: ${{ github.event.pull_request.number }}
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GITHUB_REPOSITORY: ${{ github.repository }}
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GITHUB_WORKFLOW: ${{ github.workflow }}
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GITHUB_JOB: ${{ github.job }}
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GITHUB_RUN_ID: ${{ github.run_id }}
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GITHUB_RUN_NUMBER: ${{ github.run_number }}
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GITHUB_RUN_ATTEMPT: ${{ github.run_attempt }}
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JOB_ID: ${{ steps.get-job-id.outputs.job-id }}
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JOB_NAME: ${{ steps.get-job-id.outputs.job-name }}
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BRANCH: ${{ steps.parse-ref.outputs.branch }}
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SHA1: ${{ github.event.pull_request.head.sha || github.sha }}
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BASE_SHA: ${{ github.event.pull_request.base.sha || github.sha }}
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PYTORCH_RETRY_TEST_CASES: 1
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PYTORCH_OVERRIDE_FLAKY_SIGNAL: 1
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TEST_CONFIG: ${{ matrix.config }}
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SHARD_NUMBER: ${{ matrix.shard }}
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NUM_TEST_SHARDS: ${{ matrix.num_shards }}
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REENABLED_ISSUES: ${{ steps.keep-going.outputs.reenabled-issues }}
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CONTINUE_THROUGH_ERROR: ${{ steps.keep-going.outputs.keep-going }}
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SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2
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SCCACHE_S3_KEY_PREFIX: ${{ github.workflow }}
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SHM_SIZE: ${{ contains(inputs.build-environment, 'cuda') && '2g' || '1g' }}
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DOCKER_IMAGE: ${{ inputs.docker-image }}
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XLA_CUDA: ${{ contains(inputs.build-environment, 'xla') && '0' || '' }}
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XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla
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PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: ${{ matrix.mem_leak_check && '1' || '0' }}
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PYTORCH_TEST_RERUN_DISABLED_TESTS: ${{ matrix.rerun_disabled_tests && '1' || '0' }}
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DASHBOARD_TAG: ${{ inputs.dashboard-tag }}
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HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
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run: |
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set -x
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if [[ $TEST_CONFIG == 'multigpu' ]]; then
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TEST_COMMAND=.ci/pytorch/multigpu-test.sh
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elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then
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TEST_COMMAND=.ci/onnx/test.sh
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else
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TEST_COMMAND=.ci/pytorch/test.sh
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fi
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# detached container should get cleaned up by teardown_ec2_linux
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# TODO: Stop building test binaries as part of the build phase
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# Used for GPU_FLAG since that doesn't play nice
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# shellcheck disable=SC2086,SC2090
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container_name=$(docker run \
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${GPU_FLAG:-} \
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-e BUILD_ENVIRONMENT \
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-e PR_NUMBER \
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-e GITHUB_ACTIONS \
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-e GITHUB_REPOSITORY \
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-e GITHUB_WORKFLOW \
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-e GITHUB_JOB \
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-e GITHUB_RUN_ID \
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-e GITHUB_RUN_NUMBER \
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-e GITHUB_RUN_ATTEMPT \
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-e JOB_ID \
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-e BASE_SHA \
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-e BRANCH \
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-e SHA1 \
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-e AWS_DEFAULT_REGION \
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-e IN_WHEEL_TEST \
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-e SHARD_NUMBER \
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-e TEST_CONFIG \
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-e NUM_TEST_SHARDS \
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-e REENABLED_ISSUES \
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-e CONTINUE_THROUGH_ERROR \
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-e PYTORCH_RETRY_TEST_CASES \
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-e PYTORCH_OVERRIDE_FLAKY_SIGNAL \
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-e PR_LABELS \
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-e MAX_JOBS="$(nproc --ignore=2)" \
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-e SCCACHE_BUCKET \
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-e SCCACHE_S3_KEY_PREFIX \
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-e XLA_CUDA \
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-e XLA_CLANG_CACHE_S3_BUCKET_NAME \
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-e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \
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-e PYTORCH_TEST_RERUN_DISABLED_TESTS \
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-e SKIP_SCCACHE_INITIALIZATION=1 \
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-e HUGGING_FACE_HUB_TOKEN \
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-e DASHBOARD_TAG \
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--env-file="/tmp/github_env_${GITHUB_RUN_ID}" \
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--ulimit stack=10485760:83886080 \
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--security-opt seccomp=unconfined \
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--cap-add=SYS_PTRACE \
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--ipc=host \
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--shm-size="${SHM_SIZE}" \
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--tty \
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--detach \
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--name="${container_name}" \
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--user jenkins \
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-v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \
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-w /var/lib/jenkins/workspace \
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"${DOCKER_IMAGE}"
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)
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# Propagate download.pytorch.org IP to container
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grep download.pytorch.org /etc/hosts | docker exec -i "${container_name}" sudo bash -c "/bin/cat >> /etc/hosts"
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echo "DOCKER_CONTAINER_ID=${container_name}" >> "${GITHUB_ENV}"
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docker exec -t "${container_name}" sh -c "pip install $(echo dist/*.whl)[opt-einsum] && ${TEST_COMMAND}"
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- name: Upload pytest cache if tests failed
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uses: ./.github/actions/pytest-cache-upload
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continue-on-error: true
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if: failure() && steps.test.conclusion && steps.test.conclusion == 'failure'
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with:
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cache_dir: .pytest_cache
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shard: ${{ matrix.shard }}
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sha: ${{ github.event.pull_request.head.sha || github.sha }}
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test_config: ${{ matrix.config }}
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job_identifier: ${{ github.workflow }}_${{ inputs.build-environment }}
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- name: Print remaining test logs
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shell: bash
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if: always() && steps.test.conclusion
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run: |
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cat test/**/*_toprint.log || true
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- name: Stop monitoring script
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if: always() && steps.monitor-script.outputs.monitor-script-pid
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shell: bash
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continue-on-error: true
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env:
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MONITOR_SCRIPT_PID: ${{ steps.monitor-script.outputs.monitor-script-pid }}
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run: |
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kill "$MONITOR_SCRIPT_PID"
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- name: Upload test artifacts
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uses: ./.github/actions/upload-test-artifacts
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if: always() && steps.test.conclusion && steps.test.conclusion != 'skipped'
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with:
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file-suffix: ${{ github.job }}-${{ matrix.config }}-${{ matrix.shard }}-${{ matrix.num_shards }}-${{ matrix.runner }}_${{ steps.get-job-id.outputs.job-id }}
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use-gha: ${{ inputs.use-gha }}
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- name: Collect backtraces from coredumps (if any)
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if: always()
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run: |
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# shellcheck disable=SC2156
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find . -iname "core.[1-9]*" -exec docker exec "${DOCKER_CONTAINER_ID}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \;
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- name: Store Core dumps on S3
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uses: seemethere/upload-artifact-s3@v5
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if: failure()
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with:
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name: coredumps-${{ matrix.config }}-${{ matrix.shard }}-${{ matrix.num_shards }}-${{ matrix.runner }}
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retention-days: 14
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if-no-files-found: ignore
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path: ./**/core.[1-9]*
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- name: Teardown Linux
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uses: pytorch/test-infra/.github/actions/teardown-linux@main
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if: always()
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# NB: We are currently having an intermittent GPU-related issue on G5 runners with
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# A10G GPU. Once this happens, trying to reset the GPU as done in setup-nvidia does
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# not seem to help. Here are some symptoms:
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# * Calling nvidia-smi timeouts after 60 second
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# * Fail to run nvidia-smi with an unable to determine the device handle for GPU
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# unknown error
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# * Test fails with a missing CUDA GPU error when initializing CUDA in PyTorch
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# * Run docker --gpus all fails with error response from daemon
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#
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# As both the root cause and recovery path are unclear, let's take the runner out of
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# service so that it doesn't get any more jobs
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- name: Check NVIDIA driver installation step
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if: failure() && steps.install-nvidia-driver.outcome && steps.install-nvidia-driver.outcome != 'skipped'
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shell: bash
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env:
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RUNNER_WORKSPACE: ${{ runner.workspace }}
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run: |
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set +e
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set -x
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nvidia-smi
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# NB: Surprisingly, nvidia-smi command returns successfully with return code 0 even in
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# the case where the driver has already crashed as it still can get the driver version
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# and some basic information like the bus ID. However, the rest of the information
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# would be missing (ERR!), for example:
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#
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# +-----------------------------------------------------------------------------+
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# | NVIDIA-SMI 525.89.02 Driver Version: 525.89.02 CUDA Version: 12.0 |
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# |-------------------------------+----------------------+----------------------+
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# | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
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# | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
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# | | | MIG M. |
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# |===============================+======================+======================|
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# | 0 ERR! Off | 00000000:00:1E.0 Off | ERR! |
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# |ERR! ERR! ERR! ERR! / ERR! | 4184MiB / 23028MiB | ERR! Default |
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# | | | ERR! |
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# +-------------------------------+----------------------+----------------------+
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#
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# +-----------------------------------------------------------------------------+
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# | Processes: |
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# | GPU GI CI PID Type Process name GPU Memory |
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# | ID ID Usage |
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# |=============================================================================|
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# +-----------------------------------------------------------------------------+
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#
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# This should be reported as a failure instead as it will guarantee to fail when
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# Docker tries to run with --gpus all
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#
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# So, the correct check here is to query one of the missing piece of info like
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# GPU name, so that the command can fail accordingly
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nvidia-smi --query-gpu=gpu_name --format=csv,noheader --id=0
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NVIDIA_SMI_STATUS=$?
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# These are acceptable return code from nvidia-smi as copied from setup-nvidia GitHub action
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if [ "$NVIDIA_SMI_STATUS" -ne 0 ] && [ "$NVIDIA_SMI_STATUS" -ne 14 ]; then
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echo "NVIDIA driver installation has failed, shutting down the runner..."
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.github/scripts/stop_runner_service.sh
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fi
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# For runner with multiple GPUs, we also want to confirm that the number of GPUs are the
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# power of 2, i.e. 1, 2, 4, or 8. This is to avoid flaky test issue when one GPU fails
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# https://github.com/pytorch/test-infra/issues/4000
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GPU_COUNT=$(nvidia-smi --list-gpus | wc -l)
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NVIDIA_SMI_STATUS=$?
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# These are acceptable return code from nvidia-smi as copied from setup-nvidia GitHub action
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if [ "$NVIDIA_SMI_STATUS" -ne 0 ] && [ "$NVIDIA_SMI_STATUS" -ne 14 ]; then
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echo "NVIDIA driver installation has failed, shutting down the runner..."
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.github/scripts/stop_runner_service.sh
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fi
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# Check the GPU count to be a power of 2
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if [ "$GPU_COUNT" -le 8 ] && [ "$GPU_COUNT" -ne 1 ] && [ "$GPU_COUNT" -ne 2 ] && [ "$GPU_COUNT" -ne 4 ] && [ "$GPU_COUNT" -ne 8 ]; then
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echo "NVIDIA driver detects $GPU_COUNT GPUs. The runner has a broken GPU, shutting it down..."
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.github/scripts/stop_runner_service.sh
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fi
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